Build smart language applications using Deep Learning
About This Video
Extensive practical training to understand the combined working of NLP, deep learning, and PyTorch
Work with both traditional & modern NLP tools like NLTK, SpaCy & Word2Vec for creating real world NLP models.
Each chapter includes several code examples and illustrations for an in-depth understanding of performing complex NLP tasks
In Detail
The main goal …
Hands-On Natural Language Processing with Pytorch
Video description
Build smart language applications using Deep Learning
About This Video
Extensive practical training to understand the combined working of NLP, deep learning, and PyTorch
Work with both traditional & modern NLP tools like NLTK, SpaCy & Word2Vec for creating real world NLP models.
Each chapter includes several code examples and illustrations for an in-depth understanding of performing complex NLP tasks
In Detail
The main goal of this course is to train you to perform complex NLP tasks (and build intelligent language applications) using Deep Learning with PyTorch.
You will build two complete real-world NLP applications throughout the course. The first application is a Sentiment Analyzer that analyzes data to determine whether a review is positive or negative towards a particular movie. You will then create an advanced Neural Translation Machine that is a speech translation engine, using Sequence to Sequence models with the speed and flexibility of PyTorch to translate given text into different languages.
By the end of the course, you will have the skills to build your own real-world NLP models using PyTorch's Deep Learning capabilities.
Audience
If you’re a developer, researcher or aspiring AI data scientist ready to dive deeper into this rapidly growing area of artificial intelligence then this course is for you! Some basic Machine learning background & experience in programming with Python is required.
Using Deep Learning in Natural Language Processing
Functions and Features of PyTorch
Installing and Setting Up PyTorch
Understanding Sentiment Analysis and NMT
Chapter 2 : Data Cleaning and Preprocessing for Sentiment Analysis
NLTK and spaCy Installations
Tokenization with NLTK
Stop Words
Lemmatization
Pipelines
Chapter 3 : Implement Word Embeddings with gensim
Working with Word Embeddings
Setting Up and Installing gensim
Exploring Word Embeddings with gensim
Understanding the Embeddings Created
Pretrained Embeddings Using Word2vec
Chapter 4 : Train RNNs and LSTMs Units for Sentiment Analysis
Working with Recurrent Neural Network
Implementing RNN
Results with RNN
Working with LSTM
Implementing LSTM
Results with LSTM
Chapter 5 : Build a Neural Machine Translator
Intro to seq2seq
Installations
Implementing seq2seq – Encoder
Implementing seq2seq – Decoder
Results with seq2seq
Chapter 6 : Improve the Neural Machine Translation with Attention Networks
Introduction to Attention Networks
Implementing seq2seq – Encoder
Results with Attention Network
The Way Forward
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